Culture, Tourism and the Centre for Education Statistics
The High Education / Low Income Paradox: College and University Graduates with Low Earnings, Ontario, 2006
Section 4: Data and methodology
The data source for the analysis reported here is the Survey of Labour and Income Dynamics (SLID), reference year 2006. SLID is the primary Canadian source for income and income-distribution data. The survey provides an extensive picture of individual and family financial and work situations. The target population for SLID is all individuals in Canada, excluding residents of the Yukon, the Northwest Territories and Nunavut, residents of institutions and persons living on Indian reserves. Overall, these exclusions amount to less than three percent of the population.
The Survey of Labour and Income Dynamics sample
The samples for SLID are selected from the monthly Labour Force Survey (LFS) and thus share the latter's sample design. The LFS sample is drawn from an area frame and is based on a stratified, multi-stage design that uses probability sampling. The total sample is composed of six independent samples, called rotation groups, because each month one sixth of the sample (or one rotation group) is replaced. The SLID sample is composed of two panels. Each panel consists of two LFS rotation groups and includes roughly 15,000 households. A panel is surveyed for a period of six consecutive years. A new panel is introduced every three years, so two panels always overlap (with the exception of 1993 to 1995, when SLID was first begun). Since our analysis is cross-sectional (college- and university-educated workers in low-earnings situations in 2006) we are able to create a data file that draws on two overlapping SLID panels of 25 to 64 year-olds who had non-zero employment earnings.1 The total sample size is approximately 28,000 individuals in 2006.
Cross-sectional weights account for unequal probabilities of sample selection. To account for the complex sample design, the bootstrap technique was used to estimate coefficients of variation, confidence intervals and to test for statistical significance of differences.
This study was initially prompted by the finding, based on international comparisons outlined by the Organization for Economic Cooperation and Development (OECD) (2008), that Canada ranked higher than most other key OECD countries in terms of the percentage of postsecondary graduates with earnings at or below half the national median in 2006. The OECD definition on which the comparison was based includes all individuals between the ages of 25 and 64 who had non-zero employment earnings in 2006 (the latest year of data available at the time of the OECD analysis). The definition includes all individuals who reported having employment earnings, even though working may not have been their main activity for that year.
College graduates and university graduates are treated separately in the analysis and are divided into five earnings categories in 2006.2 These are:
- Workers earning at or below half of the national median earnings (less than or equal to $16,917);
- Workers earning more than half the national median but at or below the median earnings ($16,918 to $33,834);
- Workers earning more than the national median but at or below 1.5 times the median earnings ($33,835 to $50,751);
- Workers earning more than 1.5 times the national median but at or below 2 times the median earnings ($50,752 to $67,668); and
- Workers earning more than two times the national median earnings (more than $67,669).
The value for the median earnings is calculated for the total Canadian population aged 25 to 64 with employment earnings. In 2006, this median was $33,834 before taxes and transfers.3
We examine a variety of characteristics of college and university graduates by these earnings categories to determine whether certain types of workers are over- or under-represented in each category. We do this by dividing the percentage of earners in a given category (for example, women) by the percentage of earners in that category in the total population. This method produces a ratio that, when less than one, indicates that workers with a given characteristic are under-represented in that earnings category and that when greater than one, indicates that workers with a given characteristic are over-represented in that earnings category. For example, if the ratio for university-educated women earning less than half the median is 1.5, then they are over-represented in the lowest earnings category.
Our approach is to begin by looking at demographic or 'given' characteristics of the earner. These include their age, sex, immigrant status, length of time in Canada and source region (if an immigrant), and province of residence. We then examine their labour market characteristics since an individual's work situation will directly affect how much they earn from paid employment. It could be that some highly-educated individuals are working full-time, but earning lower wages. This may be more likely in some occupations or industries than it is in others, so we investigate in which industries and occupations earnings for highly-educated workers are low.
As noted previously, while the population examined in this study reported non-zero employment earnings in 2006, this does not necessarily mean that their main activity was working. For example, the sample includes students or semi-retirees who report non-zero earnings. It could be that the worker's annual employment earnings are low because he or she does not work full-time, full-year. In other words, earnings are affected by how much labour a worker is willing to offer. As noted above, to be consistent with the OECD definition of workers in low-earnings situations, the initial motivation for the analysis reported here, the dependent variable is annual employment earnings rather than hourly earnings.4 What this means, then, is that working schedule becomes part of the possible explanatory variables – in other words, are those in low earnings situations there because they are not working full-time?
Next, we examine the schooling choices of these earners. While the population is already divided into the college- and university-educated, we further look at whether certain fields of study are more likely to be associated with low earnings. We also examine location of study. Both field of study and location of study reflect the respondent's highest educational qualification.
Finally, we consider the family and earnings situations of these workers. We examine whether the main source of income for these earners is indeed employment earnings, whether that person is the major income earner in the family, how many earners are in that family and total family earnings.
In the second part of the analysis, logistic regression analysis is used to identify the independent contributions of each of these factors to the probability of falling into low income and to determine whether the contribution of each factor is affected by the addition of other variables to the model. Since we are interested in the relative impacts of five different groups of factors (that is, demographic characteristics, province, education, major activity and industry and occupation), each group is introduced in sequence (one is born with certain characteristics, one acquires education and then one enters the labour market). All five groups of factors are included in the final model in order to determine the relative size of their impacts, after controlling for the impacts of the other factors.
- This includes the self-employed.
- For education level we use the respondent's highest level of certification. Respondents who reported both a college qualification and a university qualification were treated as a 'university graduate'. As university qualifications generally take more in-class time to complete than do college qualifications, in this report they are considered the higher level of qualification. Canada-wide, 30% of university degree holders also held a certificate or diplomas from a community college, business school, trade or vocational school or CEGEP.
- In 2006 the median in Ontario was $36,770.
- See Appendix C for a discussion of the relationship between annual and hourly earnings.
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